Self-Correcting Agents Reshape Insurance Document Extraction

Loss runs—claim history documents used to price insurance policies—present an extreme document extraction challenge due to inconsistent formatting, multi-table structures, and context-dependent data. FurtherAI's breakthrough wasn't incremental model improvement; instead, they achieved a jump from 80% to 95% accuracy by architecting agents that validate and correct their own outputs, demonstrating that self-supervision may be more powerful than raw model capability. This pattern has broader implications for any industry processing high-stakes, messy documents, suggesting a shift in how we approach structured data extraction.